Journal of Clinical and Translational Science

BackgroundEvidence supports the key role research mentors play in bolstering the success of early stage investigators (ESI). However, there are limited data about the impact of supplemental, cross-disciplinary career mentorship and professional development opportunities for ESIs seldom included during academic training. We assessed the perceived value of this approach among post-doctoral fellows and early career faculty who participated in a multi-component career mentoring program organized by the University of California, San Francisco Center for AIDS Research (UCSF CFAR). MethodsWe surveyed past program participants (2005-2020), assessing demographics, current career status, perceived impact of the program, and feedback on program elements. We performed thematic analysis on open-ended responses to explore program benefits. ResultsOf 146 program participants contacted, 102 responded (70% response rate). Over two thirds (65%) were female, and 38% self-identified as underrepresented minority (URM) investigators. A majority of respondents now dedicate >70% of their time to research. All would recommend the program to ESI colleagues, and over 80% reported that their CFAR mentors influenced their career trajectories in several ways, including help with grant writing, linkage to researchers sparking new collaborations, and support through personal challenges or navigating conflict with primary research mentors. While 90% of URM ESIs valued advice from CFAR mentors, only a third reported receiving specific support around challenges faced as minoritized investigators. ConclusionsA career mentoring program designed to complement the support offered by research mentors positively influenced the career trajectory of ESIs. Focused efforts are needed to support URM investigators who face ongoing structural barriers to success in academic settings.

IntroductionPhysician assistant (PA) programs face persistent challenges in recruiting and retaining clinical preceptors due to time constraints, administrative burden, lack of compensation, and limited training. Additional pressures, such as health care consolidation, program expansion, clinician burnout, and financial implications of paid clinical sites, further strain preceptorship capacity. This study examines motivators and barriers influencing clinicians willingness to precept PA students. MethodsThis mixed-methods study used snowball sampling to recruit current, former, and non-precepting PAs across North Carolina. Participants completed surveys with Likert-scale and open-ended items adapted from the 2011 National Survey of Physician Assistants. Four virtual focus groups, selected from survey respondents, underwent semi-structured interviews informed by Self-Determination Theory (SDT). Quantitative data were analyzed using descriptive statistics and ordinal logistic regression; qualitative data underwent thematic analysis with deductive SDT coding and inductive refinement. Triangulation integrated findings. ResultsRespondents (N = 158) represented diverse clinical experience. Top motivators included student quality (66%), program support (53%), and financial compensation (51%). Key barriers were student quality (61.29%), burnout (53.23%), and lack of compensation (46.77%). From the focused group discussion, four themes emerged: Student Quality, Financial Compensation, Non-Financial Incentives, and Administrative Support. Student preparedness acted as both motivator and barrier; compensation concerns focused on fairness. DiscussionPreceptorship relies on relational and professional factors, student quality, recognition, and institutional alignment, rather than financial incentives alone. System inefficiencies, inadequate preparation, and misaligned compensation hinder engagement. Improving student readiness, enhancing institutional support, and implementing transparent, layered incentives may strengthen recruitment and retention.

BackgroundClinical research coordinators play a crucial role in ensuring the scientific rigor, regulatory compliance, and operational integrity of clinical trials. However, in Africa, they often lack access to structured, competency-based training, especially in operational, regulatory, and trial management domains. This study evaluated the effectiveness of a comprehensive training intervention designed to standardize and enhance core competencies of clinical trial coordinators. MethodsWe conducted a prospective pre-post interventional study among cohorts of clinical research professionals completing a 10-week, internationally-accredited, Moodle-based clinical trial operations training program aligned with the Joint Task Force Core Competency Framework, covering 10 lessons and 25 domains. Self-reported competence was evaluated at baseline and post-training. Data analyses included paired t-tests for aggregate scores, McNemars exact test for domain-level proportions, multivariable logistic regression for predictors of improvement, and Cohens d for effect size. ResultsAmong the 166 participants enrolled from 19 African countries and completed the pre-training survey, 152 who completed the program and post-training survey were included. The training significantly increased the mean aggregate competence from 12.24{+/-}7.85 (out of a maximum of 25) to 23.35{+/-}2.73 (mean difference: 11.11; 95% CI 9.86-12.36; p<0.001; Cohens d=1.41). Score variance decreased, with the median score increasing from 12.0 (IQR: 6.0-19.0) to 24.5 (IQR: 23.0-25.0). All 25 domains improved (p<0.001), with the largest gains in complex, low-baseline domains: managing external partners (+59.2%), project management (+58.6%), financial management (+55.3%), and trial close-out (+57.2%). (+57.2%). Ethical principles and informed consent that had high baseline competence reached near-universal levels at 99.3% and 98.7%, respectively. No differences were observed by country or gender (p>0.05). ConclusionStructured, competency-based training strengthens clinical trial coordinators capabilities, particularly in technical and administrative domains that are often overlooked. Accredited, framework-aligned clinical trial training programs promote consistent trial quality, strengthen research capacity, and sustain excellence in clinical trial delivery. WHAT IS ALREADY KNOWN ON THIS TOPIC- Clinical research coordinators play a crucial role in ensuring the scientific rigor, regulatory compliance, and operational integrity of clinical trials WHAT THIS STUDY ADDS- The study evaluated the effectiveness of a comprehensive training intervention designed to standardize and enhance core competencies of clinical trial coordinators in Africa, where they often lack access to structured, competency-based training HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY- This study should encourage the design and delivery of internationally-accredited, Moodle-based clinical trial operations training programs in Africa that enhance clinical trial competency.

BackgroundParticipating in research during medical school is supported by institutional programs and may influence subsequent professional development. ObjectiveWe aimed to describe the current status and heterogeneity of scholarly research programs for medical students in the United States, including expectations, support, and key structural features. MethodsWe conducted a cross-sectional web audit of official webpages for all accredited US MD- and DO-granting medical schools (search performed September 2024 to January 2025). Extracted variables included participation requirements, mentorship, timing and duration (overall and dedicated research time), expected scholarly outputs, funding sources, stipend information, and stated program goals. We compared Carnegie tier R1 (Very high research activity) versus other institutions, QS Top-50 versus other institutions, and MD versus DO schools using {chi}2/Fisher exact tests for 2x2 tables and exact trend or Freeman-Halton tests for multicategory variables. ResultsPrograms were identified for all 202 institutions. Funding was explicitly mentioned by 61.9% (125/202) of programs, 27.0% (51/189) were compulsory, 98.9% (188/190) reported faculty mentorship, and 91.0% (171/188) were exclusive for medical students. Program duration, dedicated time, expected outcomes, stipend reporting, funding sources, and stated goals varied widely. Carnegie R1 institutions had longer duration (P=.002) and tended to report external funding more often than other institutions (25/104, 24.0% vs 9/98, 9.2%; OR 3.13, 95% CI 1.38-7.10; P=.008). QS Top-50 institutions were more likely to require compulsory participation than other institutions (11/19, 57.9% vs 40/170, 23.5%; OR 4.47, 95% CI 1.68-11.87; P=.003). No significant differences were observed between MD and DO programs across most measured characteristics. ConclusionsScholarly research programs for medical students are ubiquitous across US medical schools but heterogeneous in structure, expectations, and support. Research-intensive and top-ranked institutions may have more external funding and sometimes may put together longer and compulsory programs Further evaluation of student experiences and outcomes is warranted.

BackgroundDelivering timely, high-quality feedback on resident scholarly projects is labour-intensive, especially in large programmes. We developed an AI-assisted evaluation system, powered by the open-weight LLaMA-3.1 large-language model (LLM), to generate formative feedback on Family Medicine residents scholarly projects and compared its performance with expert human evaluators. MethodsWe evaluated whether the AI-generated feedback achieves comparable quality to expert feedback. The tool ingests heterogeneous resident submissions (PDFs, scans, photographs) via OCR and produces section-by-section feedback aligned with programme rubrics. In a three-phase study we evaluated 240 feedback reports (Short, Question and Timeline, Final; n = 80 each). Within each phase, 40 reports were AI-generated and 40 produced by research experts across four project types: Quality Improvement, Survey-Based, Research, and Literature Review. Blinded raters used a 25-item survey across five constructs: understanding & reasoning, trust & confidence, quality of information, expression style & persona, safety & harm. ResultsSurvey reliability was high across phases ( = .71-.98). Human feedback generally out-scored AI. In short reports, humans led on quality (Mean {+/-} SD; 4.14 {+/-} 0.57 vs 3.09 {+/-} 1.05) and trust (3.96 {+/-} 0.71 vs 2.78 {+/-} 1.15). In final reports, differences become small for quality (4.09 {+/-} 0.65 vs 3.49 {+/-} 0.68) and persona (4.16 {+/-} 0.40 vs 3.91 {+/-} 0.50), while AI was preferred for safety (4.50 {+/-} 0.60 vs 4.36 {+/-} 0.56). Performance varied by project type: in survey-based final reports the AI led on quality (4.28 {+/-} 0.50 vs 3.98 {+/-} 0.44) and safety (4.58 {+/-} 0.40 vs 4.24 {+/-} 0.67), whereas in quality-improvement short reports humans were markedly superior in reasoning (4.27 {+/-} 0.68 vs 2.33 {+/-} 1.00). ConclusionsAn open-weight LLM with curated prompts can generate rubric-aligned feedback at scale that approaches the quality of expert human feedback. While expert feedback remained superior overall, AI surpassed humans in selected contexts and safety assessments. Performance of the tool will increase over time as newer and more capable open-weight models are released. Our code and systems prompts are open source.

Digital health technologies are powerful-enhancing data collection, participant engagement, and personalized health interventions-yet their rapid proliferation has outpaced guidance for research participant protection. Current practice assists researchers in identifying risks but provides limited support for comprehensive risk management. To address this gap, we developed the Digital Health Checklist-Risk Management (DHC-RM) Tool, which integrates the established Digital Health Checklist with approaches from safety risk management. We conducted a study (n=40) comparing the DHC-RM Tool with current practice using a randomized experimental difference-in-differences design. Primary outcomes were the quantity, variety, and novelty of risks identified; secondary outcomes were the same constructs applied to risk control development. Compared with current practice, use of the DHC-RM Tool resulted in dramatically improved performance across all primary outcomes. Users identified on average 14.7 additional risks (compared to baseline) versus 0.26 in the control group and a higher number of risks in each of six pre-identified risk domains. Half of all distinct risks identified in the comparison phase were identified exclusively using the tool. The tool also improved risk control design, producing 9.63 additional risk control strategies per participant compared with 0.15 for current practice and yielding substantially greater novelty and variety. User feedback was also positive: 75% of participants reported they would use the tool again, citing its structured workflow, just-in-time examples, improved insight into risks, and its value for IRB communication. Suggestions for refinement focused primarily on expanding training examples and providing additional support for risk control development. The DHC-RM Tool significantly improves risk management practice in digital health research. By embedding structured, ethics-informed risk management into digital health research design, the DHC-RM Tool has the potential to improve participant protection while also streamlining ethics approval. Author SummaryDigital health research can put participants (and others) at risk in ways that dont always occur to the researchers who are designing a study. Researchers also face challenges in prioritizing risks and coming up with ideas to reduce those risks. We developed a new approach, the Digital Health Checklist - Risk Management Tool (DHC-RM Tool), to give researchers the support they need to identify, assess, and address research participant risks in this fast-moving field. Our experimental study found that use of the DHC-RM Tool led to a very large improvement in how well researchers managed the risks of digital health research studies. Using the toolkit, they were able to identify more risks than they identified using current practice-including risks they would not otherwise have considered. They were also able to come up with more changes to reduce the risks associated with digital health research studies, including changes they would not otherwise have considered. Those who used the toolkit found it beneficial and easy to use. The DHC-RM Tool fills an important gap in the science and practice of participant protection in digital health research.

The terms personalized, individualized and precision medicine are increasingly used to describe health interventions, yet their operational meaning in clinical research remains unclear. Despite extensive conceptual discussion, there is limited empirical evidence on how these labels are applied in randomized controlled trials (RCTs) and whether such trials meet standards of transparency and methodological rigor. We systematically examined 262 RCTs published between 2020 and 2022 that used the terms "personalized", "individualized", or "precision" in the title to describe an intervention. The term "personalized" was used most frequently (49.2%), followed by "individualized" (45.8%) and "precision" (5.0%). In most trials, personalization involved behavioral, digital, or pharmacological interventions, with few studies employing -omics approaches. Personalization was most often based on individual lifestyle factors, psychological characteristics, or disease classification. We also found that in most trials, personalization consisted of tailoring a single intervention to individuals (82.8%), often through individualized dosage (73.2%). Most included RCTs were judged to be at high risk of bias and showed limited transparency with respect to data and code sharing. Our study suggests that, in contemporary RCTs, the labels "personalized", "individualized", and "precision" are applied interchangeably to a wide range of heterogeneous interventions that are predominantly non-genomic. Greater conceptual clarity and stronger methodological standards are needed to ensure that claims of personalization in clinical research are empirically meaningful and reliable.

BackgroundChest radiograph interpretation is a foundational skill in physician associate (PA) education, and competence in diagnostic imaging is an accreditation standard. While a larger body of research on radiology education exists in undergraduate medical education, considerable variability in instructional approaches limits clear conclusions regarding the most effective method. Growing evidence supports the use of active learning strategies in radiology instruction. However, little published research specifically addresses radiology education within PA programs. Team-Based Learning (TBL), an active learning approach grounded in social constructivism that emphasizes preparation, collaboration, and application, may be well suited to teaching image interpretation. This study evaluates the effectiveness of TBL compared with traditional lecture-based instruction for chest radiograph interpretation. MethodsA mixed-methods, quasi-experimental cohort comparison using a pre-post design was conducted with two consecutive PA student cohorts at a single institution. One cohort received a 90-minute lecture-based session; another cohort participated in a 90-minute TBL session. Academic performance was assessed using validated pre- and post-tests. Student satisfaction and self-efficacy were evaluated using post-session surveys derived from the Kirkpatrick model and Banduras self-efficacy theory. Independent sample t-tests compared quantitative outcomes, and qualitative responses were analyzed thematically. ResultsBoth cohorts demonstrated improvement in chest radiograph interpretation scores, with no statistically significant differences between groups in post-test performance or score improvement (p = 0.841). Survey results indicated favorable perceptions of both instructional approaches. The TBL cohort reported significantly higher ratings for engagement and peer interaction (p = <0.001). Self-efficacy ratings were higher among TBL participants for selected confidence-related items (p=0.003, p = 0.021, p = <0.001). Qualitative responses on what contributed most to self-efficacy emphasized peer discussion in the TBL group and structured explanations in the lecture group. ConclusionsTBL produced academic performance comparable to lecture-based instruction while supporting greater learner engagement and confidence. These findings support TBL as a feasible instructional approach for chest radiograph interpretation in PA education.

BackgroundAdvanced Practice Providers (APPs) in emergency and urgent care settings experience high burnout rates, yet limited research examines cognitive factors influencing professional fulfillment. The Empowerment Dynamic framework suggests outcome-oriented thinking may protect against burnout compared to problem-oriented patterns. ObjectiveTo examine relationships between cognitive mindset orientation, professional fulfillment, and burnout among APPs while providing preliminary validation of a novel cognitive assessment instrument. MethodsCross-sectional survey of licensed APPs working in emergency departments and urgent care facilities across two health systems (July-October 2025). Professional fulfillment and burnout were measured using the Stanford Professional Fulfillment Index; cognitive orientation was assessed using a newly developed 22-item instrument. ResultsAmong 98 respondents (19.5% response rate), mean professional fulfillment was 5.8 and mean burnout was 4.5; 40.8% met burnout criteria. Professional fulfillment and burnout were inversely correlated (r = -0.62; P < .001). Problem orientation correlated positively with burnout (r = 0.56) and negatively with fulfillment (r = -0.36), while outcome orientation showed opposite patterns (burnout: r = -0.57; fulfillment: r = 0.44). In multivariable models, outcome orientation remained independently associated with lower burnout ({beta} = -1.51; P = .003) and higher fulfillment ({beta} = 1.73; P = .002). ConclusionsCognitive mindset orientation is associated with burnout and professional fulfillment among APPs. The novel assessment instrument demonstrates acceptable psychometric properties. Future longitudinal studies are needed to establish causality and evaluate cognitive interventions for burnout prevention.

BackgroundObjective Structured Clinical Examination (OSCE; Clinical Performance Examination [CPX] in South Korea) is a high-stakes assessment of clinical performance, communication, and reasoning during time-limited patient encounters. As AI-enabled virtual standardized patient (VSP) simulation and automated scoring are introduced for OSCE-like training, prospective evidence is needed on how such systems perform and are perceived when embedded in real educational workflows. MethodsWe developed CPX with Medical students Assistant for Training and Evaluation (CPX-MATE), a web-based platform integrating (1) CPX with Virtual Standardized Patient (CPX-VSP), real-time voice dialogue with a VSP using speech-to-speech (STS) models, and (2) CPX with Real-Time Evaluator (CPX-RTE), automated transcription, checklist-based scoring, and feedback from encounter audio using a Speech-to-Text model and a large language model. During an emergency medicine clerkship (Nov 2025-Jan 2026), 60 senior medical students completed two 12-min CPX encounters (VSP with acute pancreatitis; HSP with ureteral stone) with immediate CPX-RTE feedback. For CPX-VSP, students were assigned to either a full-capacity or a resource-limited STS configuration (n=30 each). Dialogue fidelity was evaluated by turn-by-turn analysis of student-VSP exchanges, classifying responses into clinically meaningful error types (tangential, oversharing, role-breaking, off-script). CPX-RTE performance was assessed by agreement (Gwets AC1) with professor real-time and resident video-based ratings using a 45-item checklist. Usability of CPX-VSP and CPX-RTE, with overall system usability scale (SUS), were surveyed, and mean per-session costs for CPX-VSP and CPX-RTE were calculated. ResultsAcross 3,282 dialogue turns, overall error rates were 1.77% versus 9.43% for full-capacity versus resource-limited STS configurations (p<0.001), driven by fewer tangential and oversharing responses; no off-script errors were observed. The mean per-session cost was $0.12 for resource-limited configuration and $0.78 for full-capacity configuration. CPX-RTE showed high agreement with human ratings (AC1=0.916 vs professor; 0.916 vs resident), with slightly different levels of agreement across four sections, and high usability across all domains (mean scores, 4.65-4.92), with a per-session cost of $0.17. CPX-MATE demonstrated good overall usability (median [IQR] of 77.5 [70.0-85.0]). ConclusionsEmbedded within a prospective clinical clerkship, CPX-MATE demonstrated operational fidelity and human-level checklist agreement as an end-to-end, voice-based AI-assisted OSCE platform. This real-world deployment supports its scalable integration as a complementary assessment tool while highlighting the importance of systematic validation and context-aware implementation in medical education.

BackgroundThe 340B Drug Pricing Program was established to expand access to care for low-income and uninsured patients by allowing safety-net hospitals and clinics to purchase outpatient drugs at discounted prices. Over time, the program has expanded substantially, raising questions about whether participating hospitals are meeting the programs intended objectives. MethodsUsing 2023 hospital financial data from the RAND Corporation, we conducted cross-sectional descriptive comparisons of 340B and non-340B hospitals nationwide. Key measures included charity care as a percentage of operating expenses, Medicaid admissions as a share of hospital days, uncompensated care, and costs associated with uninsured patients approved for charity care. Subgroup analyses also examined the performance of Disproportionate Share Hospitals (DSH), Critical Access Hospitals (CAH), Rural Referral Centers (RRC), Sole Community Hospitals (SCH), and National Cancer Institute (NCI) designated hospitals. ResultsAmong 3,999 hospitals analyzed, 340B hospitals provided, on average, lower levels of charity care than non-340B hospitals (2.16% vs. 2.82% of operating expenses) and lower costs of charity care for uninsured patients (1.60% vs. 2.26%). However, 340B hospitals served a higher proportion of Medicaid patients (19.69% vs. 17.76%). Substantial variation was observed across 340B subcategories: DSH hospitals reported the highest Medicaid utilization, while CAH hospitals reported the lowest levels of charity care and Medicaid days. ConclusionsParticipation in the 340B program does not uniformly correlate with greater provision of charity care or uncompensated care. These findings suggest a misalignment between program intent and outcomes and support the need for greater transparency, standardized eligibility criteria, and minimum charity care requirements to ensure that 340B savings directly benefit underserved populations.

IntroductionEighteenth-century medical texts document a formative period in the evolution of clinical reasoning, yet their integration into modern medical education is limited. The traditional approach to learning the history of medicine has naturally focused on passive reading, but new approaches using AI could enable learners to interrogate and simulate the historical diagnostic logic and therapeutic paradigms. More specifically, large language models (LLMs) offer an opportunity to create interactive simulations that allow experiential engagement with historical medical reasoning. MethodsWe developed a historically constrained LLM-based educational platform designed to emulate the diagnostic reasoning, language, and conceptual frameworks of an 18th-century physician. A modern GPT architecture was customized using strict instruction-based constraints and limited exclusively to a curated corpus of six foundational 17th- 18th century medical texts. Guardrails were implemented to prevent anachronistic terminology and modern medical concepts. Model outputs were evaluated qualitatively by comparing the models diagnoses and treatment plans with published diagnoses and treatment from original 18th century sources. We also applied the simulation to modern clinical vignettes for an illustrative contrast between modern and 18th century approaches. ResultsThe model generated responses that closely aligned with 18th-century medical and rhetorical style, as well as therapeutic reasoning. When presented with historical cases, the simulation demonstrated strong concordance with original diagnoses and management strategies. Secondly, when applied to modern cases, the model described period-appropriate reasoning, highlighting clear contrasts with contemporary biomedical reasoning. ConclusionsAI broadly, and more specifically LLMs configured as historically constrained simulators, can function as effective tools for learning in medical history. This approach could enable active engagement with historical clinical reasoning, fostering critical reflection on the contingent and evolving nature of medical knowledge. Such temporal simulations hold promise for medical humanities education and interdisciplinary teaching.

BackgroundAt the school of medicine of the University of Burundi, we have faced the challenge of lack of dissection facilities and cadaveric resources, and hence, we tried to use evidence-based alternative teaching methods to meet the student needs and enhance learning outcomes in anatomy. The aim of this study was to collect students perceptions regarding this innovative teaching method of anatomy and share our experience to professionals working in similar environments. MethodsWe have designed a multimodal approach where first year medical students were first exposed to the topics during lectures and practical sessions were held afterwards in small groups using four materials including: YouTube dissection videos, 3d plastic models, anatomy drawings and the 3D4Medical virtual anatomy app. A Likert scale questionnaire including questions regarding the perceived achievement of learning objectives, 3d understanding of the structures and engagement with the content was distributed to them. Moreover, we have performed paired samples contingency tables and McNemar tests to check the statistical significance of the results in comparing the different didactic methods. ResultsThe majority of the students had positive perceptions regarding the multimodal approach. They preferred the combination of lectures and practicals rather than these didactic methods used separately (p<0.001). Furthermore, regarding the tools used in the practical sessions, the combination of the tools was also significantly preferred (p<0.001). The virtual anatomy app was significantly superior to the YouTube dissection videos in perceived achievement of 3d understanding (p=0.018). Moreover, the students agreed that being taught anatomy by surgeons has helped in bringing in more context that is useful to transfer the learnt knowledge into real life situations (p<0.001). ConclusionEfficient teaching of anatomy without cadaveric materials can be achieved by combining multiple didactic tools that promote active learning and enhance 3d understanding of the material.

BackgroundWith growing efforts aimed at optimizing health care services by reducing "low value care", medical deprescribing represents a critical policy challenge at the intersection of clinical quality, fiscal sustainability, and environmental stewardship. Despite growing evidence of its benefits, France lacks a comprehensive national framework for systematic medication review and deprescribing implementation. ObjectiveTo identify areas of consensus and divergence among key French stakeholders using an adapted Policy Delphi approach to inform national deprescribing policy development. MethodsAn exploratory survey was conducted among stakeholders across five groups (healthcare professionals, patients, academia, policymakers, and the pharmaceutical industry). Consensus levels were assessed using graded Likert scales and analysed across policy domains, including knowledge and training, collaboration, resources, policy support, and sustainability opportunities. ResultsHigh consensus emerged around knowledge gaps, the need for interprofessional collaboration, and clinical benefits of deprescribing. Moderate consensus existed regarding resource constraints and environmental sustainability. Divergence was observed between professionals/academia and policymakers/industry regarding financial incentives and regulatory readiness. A policy Delphi heatmap revealed specific alignment patterns that could serve as policy entry points. ConclusionsMulti-stakeholder consensus mapping provides an innovative governance tool for identifying actionable policy opportunities and contributes to recent tools aimed at reducing low-value care. High-consensus domains, including training, patient safety, and sustainability, offer immediate entry points for coalition-building. On the contrary, areas of divergence require structured dialogue and iterative policy learning among Frances fragmented governance structures to translate stakeholder alignment into systematic deprescribing implementation.

BackgroundThe cognitive demands of medical education require optimal learning environments. While the influence of background music on cognition has been widely studied, existing research exhibits a significant Eurocentric bias, predominantly focusing on Western classical music like the "Mozart Effect." This leaves a critical gap in understanding the impact of culturally salient, non-Western musical traditions on learning within their native contexts. MethodsA single-blind, randomized controlled trial was conducted with 147 clinical (4th, 5th and 6th) year medical students stratified by ethnicity at the Faculty of Medicine, Nnamdi Azikiwe University, Nnewi Campus, Anambra State, Nigeria between March and September 2025. Participants were randomly assigned to one of three background music conditions: Igbo Highlife (instrumental), Western classical (Mozarts Sonata K.448), or silence (control with pink noise masking). Cognitive performance was assessed through a short-term memory recall test of 20 medical terms and a timed clinical problem-solving task comprising 20 multiple-choice questions. Baseline mood was controlled for using the Positive and Negative Affect Schedule (PANAS). Data were analyzed using ANOVA and post-hoc Tukey HSD tests. ResultsMusic condition had a highly significant effect on student performance (p < 0.001). The Igbo Highlife group demonstrated superior outcomes, achieving the highest scores in memory recall (mean = 16.7) and problem-solving accuracy (mean = 15.7), alongside the fastest completion time (23.4 seconds/question), significantly outperforming both the classical and silence groups. A significant correlation was found between cultural familiarity with Highlife and enhanced cognitive performance (accuracy: {rho} = 0.268, p = 0.001). ConclusionsIncorporating music that holds cultural significance and familiarity to learners, specifically Igbo Highlife, is a highly effective auditory stimulus for enhancing learning efficiency in medical education. Students and educational institutions should consider integrating culturally familiar instrumental music into study environments to optimize cognitive performance and learning outcomes. Competing Interest StatementThe authors have declared no competing interest.

BackgroundCompetence-Based Education (CBE) in physiotherapy aims to equip graduates with essential capabilities for safe and effective practice. Frameworks often include domains like clinical reasoning, communication, and professionalism. Despite its alignment with healthcare needs, CBE implementation in higher education remains inconsistent. Many educators still rely on behaviourist paradigms focused on passive learning and binary assessments, which inadequately reflect professional competence. Constructivist and progressive models offer more suitable alternatives yet are underutilized. Objective: This study explores the feasibility of integrating programmatic assessment (PA) to better support capability development and learner-centred education. MethodThis randomized controlled trial will be conducted a University of Applied Sciences across two campuses in Switzerland. Students from Cohort PHY25 enrolled in the BSc Physiotherapy program will be included. Students are assigned to PA in two formats, individual coaching (IG A) and group coaching (IG B), or to a sham PA without any coaching or reflective support (CG). Feasibility will be evaluated through session attendance, completion of all program components, and implementation fidelity. Secondary outcomes include staff readiness, wellbeing, workload, and learning gain. DiscussionThis study explores the feasibility and educational impact of implementing programmatic assessment in undergraduate physiotherapy education. If successful, PA may enhance competence development. Findings will inform curricular redesigns and support the shift toward learner-centred, capability-based assessment strategies in health professions education. Trial registrationRegistry of Efficacy and Effectiveness Studies under the number: #25261.2v1.

IntroductionTherapeutic communication is the basis of nursing care yet it has been considered so stressful by student nurses with only 3.5% of nursing students in Kampala Uganda exhibiting optimum therapeutic communication competence. This has been attributed to inadequate training. Faculty must explore means to impart these skills in nursing students. This study implemented and evaluated an educational intervention module on therapeutic communication skills for nurses for incorporation into their teaching learning activities. MethodsA one-group pre-post quasi-experimental study was conducted with 41 diploma extension nursing students, selected via census sampling. Data were collected using self-administered structured questionnaires (content validity = 0.98; Cronbachs = 0.96), on students knowledge and perceived confidence in performing therapeutic communication. Observation checklists were used to evaluate students ability to establish nurse-patient relationships and deliver bad news in the skills lab, both before and after the intervention. ResultsThere was a significant improvement of knowledge scores from 4 (IQR: 3, 5) to 8.0 (IQR: 7.0, 9.0), (P value <0.001); perceived confidence in practicing therapeutic communication scores from 144.0 (IQR: 136.0, 153.0) to 164.0 (IQR: 155.0, 174.0) (P value <0.001); ability to initiate a nurse-patient relationship from 12.0 (IQR: 10.0, 14.0) to 17.0 (15.0, 18.0) (P value <0.001); and the ability to break bad news to the patient/caretaker from 9.0 (IQR: 7.0, 12.0) to 16.0 (14.0, 18.0) (P value <0.001) after the intervention. All scores improved in all categories of sex, program and semester of study for all participants apart from participants in the first semester of study. ConclusionThis study offers preliminary evidence that the educational intervention improves nursing students therapeutic communication skills. Further longitudinal research is needed to assess the sustained effectiveness of the module, the teaching methods used, and patients perspectives on students TC competence.

ImportanceFunding of womens health research has been low, with a narrow focus on what is considered womens health. Understanding which lifespan stages and areas of womens health are funded is essential to determine the breadth of womens health research and identify where gaps in research are concentrated. ObjectiveTo examine which lifespan stages and areas of womens health were more likely to be funded in open Canadian grant competitions. Evidence ReviewPublicly available funded Canadian Institutes of Health Research (CIHR) Project Grant abstracts from 2009 and 2023 were coded for mention of a hormonal transition period (puberty, menstrual cycle, pregnancy/postpartum, perimenopause/menopause), exogenous hormone use (hormonal contraception, fertility treatments, menopause hormone therapy), and/or a female-specific health condition. Abstracts were also coded for Indigenous health and Two Spirit, Lesbian, Gay, Bisexual, Trans, Transgender or Trans Identified, Queer, Intersex, Asexual, Plus (S2/LGBTQIA+) populations. Remaining grant abstracts were grouped by common theme.Abstracts were analyzed for changes in research representation and funding over time and whether funding was lower than expected based on population prevalence or proportion of the lifespan spent in that stage. FindingsNearly 50% of female-specific research focused on cancers (breast, gynecologic) or pregnancy and did not significantly increase in funding or representation over time. Of the funded grant abstracts that focused on pregnancy, ~22% examined outcomes pertaining only to the fetus/offspring, not the birthing parent. Over 15 years, 2.37% of all CIHR abstracts over 15 years were devoted to pregnancy, whereas only 0.24% was devoted to other hormonal life stages (menstrual cycles, menopause). For all hormonal transition stages except pregnancy, the proportion of grants and funding devoted to that stage was lower than expected based on the proportion of the lifespan spent in that stage. Conclusions and RelevanceThese findings reflect the narrow breadth of womens health, which largely focused on cancers (breast, gynecologic) or pregnancy, rather than being distributed across key life course stages that shape womens health. To advance science for all, the heterogeneity and complexity in womens health across the lifespan must be embraced and barriers for womens health research must be removed. Key PointsO_ST_ABSQuestionC_ST_ABSWhich areas and life stages of womens health are most likely to be funded in Canadian open grant competitions, and where are funding gaps concentrated? FindingsNearly half of female-specific grants focused on cancer or pregnancy, with little change over time. Pregnancy dominated hormonal-stage research, often excluding maternal outcomes, while menstrual and menopausal stages were rarely funded. For most life stages, funding was lower than expected based on lifespan representation. MeaningWomens health research funding remains narrowly focused. Broader, life-course-inclusive investment is needed to address critical gaps and advance equitable health science.

BackgroundVision loss represents a significant public health concern according to the World Health Organisation, with increasing global age-standardised prevalence rates. Visual impairments are disproportionately distributed, occurring eight times more frequently in Sub-Saharan Africa and Southeast Asia compared to high-income regions. The Interprofessional Education (IPE) approach, utilizing the Arclight package as an implementation vehicle, offers promising potential for collaborative early detection and management of eye conditions in resource-constrained environments. This research aimed to implement validated Interprofessional Eye Health Education(IPEHE) in Rwanda, assess fundamental eye health knowledge and skills acquisition, evaluate medium to long-term learning retention, and explore IPEs role in developing these competencies. MethodsThe study employed a mixed-methods approach combining a Randomised Controlled Trial (RCT) with qualitative assessment at the University of Rwanda. Researchers invited 443 final-year students from diverse healthcare programs including nursing, pharmacy, midwifery, medicine, and ophthalmic clinical officers. With statistical power set at 0.80 and alpha error probability at 0.05, the design aimed to detect pre-post training score differences of 10% or greater. The calculated sample size of 54 participants per group was expanded to 280 total participants (180 intervention, 100 control) to accommodate potential attrition. ResultsIn the intervention group, 161 students (89.4%) attended the training, and 113 (70.2%) participated in the 10-month follow-up assessment (POST2). Of the control group, 90 participants (90%) attended assessments at 10 months post-intervention (POST2). Knowledge scores in the intervention group increased by 58.9% (SD=20.8, Z=10.82 p<0.001) immediately post-training, while skills improved by 49.7% (SD=14, Z=-8.382, P<0.001). At the 10-month follow-up, these gains remained significantly above baseline levels. Intervention participants significantly outperformed the control group at follow-up in both knowledge, with a 54.1% difference (SE= 2.0, df (201) = 27.3, P<0.001), and skills, with a 44% difference (SE=1.1, t(155)=38.7, p<0.001). Qualitative data from the intervention group indicated an appreciation for interprofessional collaboration, holistic patient care approaches, and the practical skills acquired. ConclusionThe IPEHE intervention significantly enhanced collaborative eye health knowledge and skills among Rwandan healthcare students, with demonstrated retention up to 10 months post-intervention. These findings suggest that pre-qualification interprofessional education effectively produces collaborative practice-ready professionals capable of addressing eye health challenges in resource-limited settings.

BackgroundHealth technology assessment (HTA) agencies issue reimbursement recommendations that determine patient access to new therapies. Predicting these outcomes would enable sponsors to optimize market access strategies and health systems to anticipate budget impacts. However, traditional machine learning approaches require extensive manual feature extraction and predict only categorical outcomes, not the specific conditions attached to recommendations. MethodsWe developed Monte Carlo Committee Simulation, a neurosymbolic system that simulates multi-panelist deliberation using 14 persona-conditioned large language model panelists with weighted voting and uncertainty quantification. We conducted a temporal external validation study on CDA-AMC (Canadas Drug Agency) sponsor-submitted recommendations published between October 2024 and December 2025 (n=67), after the knowledge cutoff of the underlying models, ensuring predictions reflected reasoning rather than memorization. The system predicted both recommendation category (Reimburse with Conditions, Do Not Reimburse) and five condition categories (Population Restrictions, Prescriber/Setting Requirements, Continuation Conditions, Economic Conditions, Evidence Conditions). ResultsOn submissions where the system expressed confidence (n=44), recommendation prediction achieved 93.2% accuracy (95% CI: 84.1-100.0%), exceeding the 91.8% (95% CI: 83.7-98.0%) majority class baseline. The system demonstrated superior discrimination versus chance level (AUROC 0.817, 95% CI: 0.45-0.99, vs 0.500) and calibrated confidence estimates (ECE = 0.091). Pre-specified Strength of Mandate stratified accuracy from 96.8% (High, 95% CI: 90.3-100.0%) to 40.0% (Weak, 95% CI: 0.0-80.0%), with 83.3% of errors occurring in cases flagged as uncertain (p=0.0025). Analysis of the 5 abstained cases confirmed 40.0% accuracy, validating the systems identification of uncertain predictions. For condition prediction, the system achieved 48.8% subset accuracy, requiring correct simultaneous prediction of all 5 condition categories (25 = 32 possible combinations), and 86.3% Hamming accuracy versus 25.8% for a no-conditions baseline. Per-category accuracy ranged from 68.3% (Continuation Conditions) to 97.6% (Economic Conditions), with Continuation Conditions demonstrating the strongest discriminative ability (AUROC 0.896, 95% CI: 0.79-0.98). ConclusionsMonte Carlo Committee Simulation enables a shift from reactive to proactive market access: anticipating specific reimbursement conditions before committee review, with calibrated confidence that identifies which predictions to trust. Validated on temporally separated data the models could not have memorized, the system can be positioned as a forecasting aid that complements rather than replaces human deliberation.